import json
from typing import Dict, List, Optional, Tuple, Union

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .tokenization_led import LEDTokenizer


lowerCamelCase__  =					logging.get_logger(__name__)


lowerCamelCase__  =					{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

lowerCamelCase__  =					{
    '''vocab_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''allenai/led-base-16384''': '''https://huggingface.co/allenai/led-base-16384/resolve/main/tokenizer.json''',
    },
}

lowerCamelCase__  =					{
    '''allenai/led-base-16384''': 1_6384,
}




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					VOCAB_FILES_NAMES
 __A			       =					PRETRAINED_VOCAB_FILES_MAP
 __A			       =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 __A			       =					LEDTokenizer
 __A			       =					['''input_ids''', '''attention_mask''']







 def __init__(		self				:   List[str]							, lowercase_				:   Dict=None							, lowercase_				:   str=None							, lowercase_				:   Optional[Any]=None							, lowercase_				:   Optional[Any]="replace"							, lowercase_				:   int="<s>"							, lowercase_				:   Union[str, Any]="</s>"							, lowercase_				:   Optional[int]="</s>"							, lowercase_				:   List[Any]="<s>"							, lowercase_				:   Union[str, Any]="<unk>"							, lowercase_				:   Any="<pad>"							, lowercase_				:   Union[str, Any]="<mask>"							, lowercase_				:   List[Any]=False							, lowercase_				:   Tuple=True							, **lowercase_				:   Optional[int]							, )						->	List[str]:


   """simple docstring"""







   super().__init__(
       lowercase_							, lowercase_							, tokenizer_file=lowercase_							, errors=lowercase_							, bos_token=lowercase_							, eos_token=lowercase_							, sep_token=lowercase_							, cls_token=lowercase_							, unk_token=lowercase_							, pad_token=lowercase_							, mask_token=lowercase_							, add_prefix_space=lowercase_							, trim_offsets=lowercase_							, **lowercase_							, )

   _UpperCamelCase           =    json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
   if pre_tok_state.get("add_prefix_space"							, lowercase_) != add_prefix_space:
     _UpperCamelCase           =    getattr(lowercase_							, pre_tok_state.pop("type"))
     _UpperCamelCase           =    add_prefix_space
     _UpperCamelCase           =    pre_tok_class(**lowercase_)

   _UpperCamelCase           =    add_prefix_space

   # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
   _UpperCamelCase           =    "post_processor"
   _UpperCamelCase           =    getattr(self.backend_tokenizer							, lowercase_							, lowercase_)
   if tokenizer_component_instance:
     _UpperCamelCase           =    json.loads(tokenizer_component_instance.__getstate__())

     # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
     if "sep" in state:
       _UpperCamelCase           =    tuple(state["sep"])
     if "cls" in state:
       _UpperCamelCase           =    tuple(state["cls"])

     _UpperCamelCase           =    False

     if state.get("add_prefix_space"							, lowercase_) != add_prefix_space:
       _UpperCamelCase           =    add_prefix_space
       _UpperCamelCase           =    True

     if state.get("trim_offsets"							, lowercase_) != trim_offsets:
       _UpperCamelCase           =    trim_offsets
       _UpperCamelCase           =    True

     if changes_to_apply:
       _UpperCamelCase           =    getattr(lowercase_							, state.pop("type"))
       _UpperCamelCase           =    component_class(**lowercase_)
       setattr(self.backend_tokenizer							, lowercase_							, lowercase_)







 @property
 # Copied from transformers.models.bart.tokenization_bart_fast.BartTokenizerFast.mask_token with BART->LED
 def 		__UpperCAmelCase				(		self				:   Dict)						->	str:


   """simple docstring"""







   if self._mask_token is None:
     if self.verbose:
       logger.error("Using mask_token, but it is not set yet.")
     return None
   return str(self._mask_token)







 @mask_token.setter
 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   Union[str, Any])						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    AddedToken(lowercase_							, lstrip=lowercase_							, rstrip=lowercase_) if isinstance(lowercase_							, lowercase_) else value
   _UpperCamelCase           =    value







 def 		__UpperCAmelCase				(		self				:   Any							, *lowercase_				:   Tuple							, **lowercase_				:   List[Any])						->	BatchEncoding:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.get("is_split_into_words"							, lowercase_)

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
         "to use it with pretokenized inputs.")

   return super()._batch_encode_plus(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   List[Any]							, *lowercase_				:   str							, **lowercase_				:   Optional[int])						->	BatchEncoding:


   """simple docstring"""







   _UpperCamelCase           =    kwargs.get("is_split_into_words"							, lowercase_)

   if is_split_into_words and not self.add_prefix_space:
     raise ValueError(
         f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
         "to use it with pretokenized inputs.")

   return super()._encode_plus(*lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   str							, lowercase_				:   Optional[str] = None)						->	Tuple[str]:


   """simple docstring"""







   _UpperCamelCase           =    self._tokenizer.model.save(lowercase_							, name=lowercase_)
   return tuple(lowercase_)







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   Union[str, Any]							, lowercase_				:   int=None)						->	Tuple:


   """simple docstring"""







   _UpperCamelCase           =    [self.bos_token_id] + token_ids_a + [self.eos_token_id]
   if token_ids_a is None:
     return output

   return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







 def 		__UpperCAmelCase				(		self				:   Optional[int]							, lowercase_				:   List[int]							, lowercase_				:   Optional[List[int]] = None)						->	List[int]:


   """simple docstring"""







   _UpperCamelCase           =    [self.sep_token_id]
   _UpperCamelCase           =    [self.cls_token_id]

   if token_ids_a is None:
     return len(cls + token_ids_a + sep) * [0]
   return len(cls + token_ids_a + sep + sep + token_ids_a + sep) * [0]







 def 		__UpperCAmelCase				(		self				:   str							, lowercase_				:   Union[Dict[str, EncodedInput], BatchEncoding]							, lowercase_				:   Optional[int] = None							, lowercase_				:   PaddingStrategy = PaddingStrategy.DO_NOT_PAD							, lowercase_				:   Optional[int] = None							, lowercase_				:   Optional[bool] = None							, )						->	dict:


   """simple docstring"""







   _UpperCamelCase           =    super()._pad(
       encoded_inputs=lowercase_							, max_length=lowercase_							, padding_strategy=lowercase_							, pad_to_multiple_of=lowercase_							, return_attention_mask=lowercase_							, )

   # Load from model defaults
   if return_attention_mask is None:
     _UpperCamelCase           =    "attention_mask" in self.model_input_names

   if return_attention_mask and "global_attention_mask" in encoded_inputs:
     _UpperCamelCase           =    encoded_inputs[self.model_input_names[0]]
     # `global_attention_mask` need to have the same length as other (sequential) inputs.
     _UpperCamelCase           =    len(encoded_inputs["global_attention_mask"]) != len(lowercase_)

     if needs_to_be_padded:
       _UpperCamelCase           =    len(lowercase_) - len(encoded_inputs["global_attention_mask"])

       if self.padding_side == "right":
         # Use `-1` since `0` in `global_attention_mask` means `local attention` instead of `not to attend`
         _UpperCamelCase           =    (
             encoded_inputs["global_attention_mask"] + [-1] * difference
         )
       elif self.padding_side == "left":
         _UpperCamelCase           =    [-1] * difference + encoded_inputs[
             "global_attention_mask"
         ]
       else:
         raise ValueError("Invalid padding strategy:" + str(self.padding_side))

   return encoded_inputs





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_longformer''': [
        '''LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
        '''LongformerConfig''',
        '''LongformerOnnxConfig''',
    ],
    '''tokenization_longformer''': ['''LongformerTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''LongformerTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''LongformerForMaskedLM''',
       '''LongformerForMultipleChoice''',
       '''LongformerForQuestionAnswering''',
       '''LongformerForSequenceClassification''',
       '''LongformerForTokenClassification''',
       '''LongformerModel''',
       '''LongformerPreTrainedModel''',
       '''LongformerSelfAttention''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFLongformerForMaskedLM''',
       '''TFLongformerForMultipleChoice''',
       '''TFLongformerForQuestionAnswering''',
       '''TFLongformerForSequenceClassification''',
       '''TFLongformerForTokenClassification''',
       '''TFLongformerModel''',
       '''TFLongformerPreTrainedModel''',
       '''TFLongformerSelfAttention''',
   ]


if TYPE_CHECKING:
   from .configuration_longformer import (
       LONGFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       LongformerConfig,
       LongformerOnnxConfig,
   )
   from .tokenization_longformer import LongformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_longformer_fast import LongformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_longformer import (
          LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          LongformerForMaskedLM,
          LongformerForMultipleChoice,
          LongformerForQuestionAnswering,
          LongformerForSequenceClassification,
          LongformerForTokenClassification,
          LongformerModel,
          LongformerPreTrainedModel,
          LongformerSelfAttention,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_longformer import (
          TF_LONGFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFLongformerForMaskedLM,
          TFLongformerForMultipleChoice,
          TFLongformerForQuestionAnswering,
          TFLongformerForSequenceClassification,
          TFLongformerForTokenClassification,
          TFLongformerModel,
          TFLongformerPreTrainedModel,
          TFLongformerSelfAttention,
      )

else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




